code: def conformer_rnnt_model(
      *,
      input_dim: int,
      encoding_dim: int,
      time_reduction_stride: int,
      conformer_input_dim: int,
      conformer_ffn_dim: int,
      conformer_num_layers: int,
      conformer_num_heads: int,
      conformer_depthwise_conv_kernel_size: int,
      conformer_dropout: float,
      num_symbols: int,
      symbol_embedding_dim: int,
      num_lstm_layers: int,
      lstm_hidden_dim: int,
      lstm_layer_norm: int,
      lstm_layer_norm_epsilon: int,
      lstm_dropout: int,
      joiner_activation: str,
  ) -> RNNT:
      r"""Builds Conformer-based recurrent neural network transducer (RNN-T) model.
  
      Args:
          input_dim (int): dimension of input sequence frames passed to transcription network.
          encoding_dim (int): dimension of transcription- and prediction-network-generated encodings
              passed to joint network.
          time_reduction_stride (int): factor by which to reduce length of input sequence.
          conformer_input_dim (int): dimension of Conformer input.
          conformer_ffn_dim (int): hidden layer dimension of each Conformer layer's feedforward network.
          conformer_num_layers (int): number of Conformer layers to instantiate.
          conformer_num_heads (int): number of attention heads in each Conformer layer.
          conformer_depthwise_conv_kernel_size (int): kernel size of each Conformer layer's depthwise convolution layer.
          conformer_dropout (float): Conformer dropout probability.
          num_symbols (int): cardinality of set of target tokens.
          symbol_embedding_dim (int): dimension of each target token embedding.
          num_lstm_layers (int): number of LSTM layers to instantiate.
          lstm_hidden_dim (int): output dimension of each LSTM layer.
          lstm_layer_norm (bool): if ``True``, enables layer normalization for LSTM layers.
          lstm_layer_norm_epsilon (float): value of epsilon to use in LSTM layer normalization layers.
          lstm_dropout (float): LSTM dropout probability.
          joiner_activation (str): activation function to use in the joiner.
              Must be one of ("relu", "tanh"). (Default: "relu")
  
          Returns:
              RNNT:
                  Conformer RNN-T model.
      """
      encoder = _ConformerEncoder(
          input_dim=input_dim,
          output_dim=encoding_dim,
          time_reduction_stride=time_reduction_stride,
          conformer_input_dim=conformer_input_dim,
          conformer_ffn_dim=conformer_ffn_dim,
          conformer_num_layers=conformer_num_layers,
          conformer_num_heads=conformer_num_heads,
          conformer_depthwise_conv_kernel_size=conformer_depthwise_conv_kernel_size,
          conformer_dropout=conformer_dropout,
      )
      predictor = _Predictor(
          num_symbols=num_symbols,
          output_dim=encoding_dim,
          symbol_embedding_dim=symbol_embedding_dim,
          num_lstm_layers=num_lstm_layers,
          lstm_hidden_dim=lstm_hidden_dim,
          lstm_layer_norm=lstm_layer_norm,
          lstm_layer_norm_epsilon=lstm_layer_norm_epsilon,
          lstm_dropout=lstm_dropout,
      )
      joiner = _Joiner(
          encoding_dim, num_symbols, activation=joiner_activation
      )
      return RNNT(encoder, predictor, joiner)
  
imports: import math
  from typing import Dict, List, Optional, Tuple
  import torch
  from torchaudio.models import Conformer, RNNT
  from torchaudio.models.rnnt import _Joiner, _Predictor, _TimeReduction, _Transcriber
label_desc: Use image node_id 1 for calling a global function with example usage: conformer_rnnt_model() and returns: RNNT